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* Update Makefile to detect AVX512 support and add compiler flags if it's available
* Based on existing AVX2 implementation, dot product on one 32-value block of 4-bit quantized ints at a time
* Perform 8 bit -> 16 bit sign extension and multiply+add on 32 values at time instead of 16
* Use built-in AVX512 horizontal reduce add to get sum at the end
* Manual unrolling on inner dot product loop to reduce loop counter overhead
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I think this is what is used in the Python code
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The readme tells people to use the command line option "-t 8", causing 8
threads to be started. On systems with fewer than 8 cores, this causes a
significant slowdown. Remove the option from the example command lines
and use /proc/cpuinfo on Linux to determine a sensible default.
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* Add AVX2 version of ggml_vec_dot_q4_1
* Small optimisations to q4_1 dot product (@Const-me)
* Rearrange Q4_1 quantization to work for multipart models. (Fix #152)
* Fix ggml_vec_mad_q4_1 too
* Fix non-vectorised q4_1 vec mul
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* add ggml_rms_norm
* update op num
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Without "static" prefix, it fails to compile in clang
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* Don't use vdotq_s32 if it's not available
`dotprod` extensions aren't available on some ARM CPUs (e.g. Raspberry Pi 4), so check for them and only use them if they're available.
Reintroduces the code removed in 84d9015 if `__ARM_FEATURE_DOTPROD` isn't defined.
* Update ggml.c
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Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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* 10% performance boost on ARM
* Back to original change
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This reverts commit 113a9e83ebc0f788f861394437087bf3ca0e019b.
There are some reports for illegal instruction.
Moved this stuff to vdotq_s32 branch until resolve
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* Apply fixes suggested to build on windows
Issue: https://github.com/ggerganov/llama.cpp/issues/22
* Remove unsupported VLAs
* MSVC: Remove features that are only available on MSVC C++20.
* Fix zero initialization of the other fields.
* Change the use of vector for stack allocations.
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